Please use this identifier to cite or link to this item: https://doi.org/10.1142/S175882510900006X
Title: Material characterization based on instrumented and simulated indentation tests
Authors: Liu, Z.
Harsono, E.
Swaddiwudhipong, S. 
Keywords: finite element
Indentation test
mechanical property
reverse analysis
uniqueness
Issue Date: Mar-2009
Source: Liu, Z., Harsono, E., Swaddiwudhipong, S. (2009-03). Material characterization based on instrumented and simulated indentation tests. International Journal of Applied Mechanics 1 (1) : 61-84. ScholarBank@NUS Repository. https://doi.org/10.1142/S175882510900006X
Abstract: This paper reviews various techniques to characterize material by interpreting load-displacement data from instrumented indentation tests. Scaling and dimensionless analysis was used to generalize the universal relationships between the characteristics of indentation curves and their material properties. The dimensionless functions were numerically calibrated via extensive finite element analysis. The interpretation of load-displacement curves from the established relationships was thus carried out by either solving higher order functions iteratively or employing neural networks. In this study, the advantages and disadvantages of these techniques are highlighted. Several issues in an instrumented indentation test such as friction, size effect and uniqueness of reverse analysis algorithms are discussed. In this study, a new reverse algorithm via neural network models to extract the mechanical properties by dual Berkovich and spherical indentation tests is introduced. The predicted material properties based on the proposed neural network models agree well with the numerical input data. © 2009 Imperial College Press.
Source Title: International Journal of Applied Mechanics
URI: http://scholarbank.nus.edu.sg/handle/10635/65785
ISSN: 17588251
DOI: 10.1142/S175882510900006X
Appears in Collections:Staff Publications

Show full item record
Files in This Item:
There are no files associated with this item.

SCOPUSTM   
Citations

15
checked on Dec 6, 2017

WEB OF SCIENCETM
Citations

14
checked on Nov 17, 2017

Page view(s)

23
checked on Dec 10, 2017

Google ScholarTM

Check

Altmetric


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.